Analysis of the upper-truncated Weibull distribution for wind speed

Kantar, Yeliz Mert; Usta, İlhan

doi:10.1016/j.enconman.2015.02.063

Cited by 78 publications

(45 citation statements)

References 40 publications

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“…12 Similarly, Weibull, gamma, inverse normal, lognormal, Gumbel, generalized extreme value, Nakagami, and generalized logistic distribution were directly compared in Algeria. 14 A further variant of the Weibull distribution is the upper-truncate Weibull distribution, 15 which can be applied in situations in which the range of the random variable is bounded from above by an unknown cutoff point, called a truncation point. To show the effectiveness of this method, the lognormal, Weibull, Rayleigh, exponential, Burr, gamma, inverse Gaussian, and inverse gamma distributions were considered and applied to model the wind speed regimes in Malaysia.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

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A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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Summary The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R2 and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.

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Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…This equation has two solution roots, of which only the positive one allows respecting constraint 4), Equation (15).…”

Section: Determination Of the Parameter K Imentioning

confidence: 99%

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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“…Aksi durumda dağılıma dayalı rüzgar gücü hesaplamalarında nümerik integral hesabı yapılması gerekmektedir [24].…”

Section: Nakagami Dağılımı (Nd)unclassified

Farkli Olasilik Yoğunluk Fonksi̇yonlari Kullanarak Rüzgar Gücü Potansi̇yeli̇ni̇n Tahmi̇ni̇

Usta¹,

Kantar²

2016

deufmd

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ÖZSürekli rassal değişken olarak kabul edilen rüzgâr hızı istatistiksel dağılımlarla karakterize edilir ve ortalama rüzgâr gücü tahminleri yapılır. Weibull dağılımı rüzgar enerjisi alanında kabul edilmiş dağılımdır. Ancak Weibull dağılımının doğa da karşılaşılan tüm rüzgar tiplerini modelleyemediği bilimsel çalışmalar yardımıyla bilinmektedir. Bu çalışmada, Weibull, Rayleigh, Log-normal, Gamma, Genelleşmiş Gamma dağılımları ve daha önce enerji alanında kullanılmamış olan Nakagami dağılımının performası çeşitli kriterler yardımıyla değerlendirilmiştir. Türkiye'nin farklı bölgelerinde ölçülen rüzgar hızı verileri üzerinde ele alınan dağılımların performansları araştırılmış, yapılan analizlerin sonucu olarak Nakagami dağılımının performasının yüksek olduğu gözlemlenmiştir. Böylece, Nakagami dağılımının rüzgar enerjisi alanında alternatif bir dağılım olarak kullanılabileceği sonucuna ulaşılmıştır.Anahtar Kelimeler: Rüzgar hızı, Nakagami dağılımı, rüzgar hızı dağılımları, model seçim kriterleri.

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“…al. [12] told that, Accurately modeling wind speed is critical in estimating the wind energy potential of a certain region. In order to model wind speed data smoothly, several statistical distributions have been studied.…”

Section: Introductionmentioning

confidence: 99%

A Review Paper on Wind Climate Modeling

2016

Arimpie - 2016

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Abstract-India has a mixed extreme wind climate which is mainly affected by thunderstorms, cyclones, gales from Monsoon winds and localised storms. Different storms occur due to different geophysical reasons and their turbulent intensities are different. Mainly tall slender structures which are located in urban region of Indian coastal areas are strongly affected by extreme wind. Every year the statistics of damages clearly reveals that even today structures and structural components are not sufficiently wind hazard resistant. So extreme events from each storm type should be fitted in a separate extreme value distribution and later on a combination of probability distributions should be made in order to specify design wind speed for a target non exceedance probability. Most of the researchers conclude that the extreme value distributions include type I(Gumbel), type II(Fre'chet) and type III (Reverse Weibull) distributions. The design wind load is proportional to the square of the design wind speed. So the specification of design wind speed is a prerequisite for the specification of design wind load. The specification of design wind speed can be done by extreme wind climate modeling.

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Analysis of the upper-truncated Weibull distribution for wind speed

Cited by 78 publications

References 40 publications

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

Farkli Olasilik Yoğunluk Fonksi̇yonlari Kullanarak Rüzgar Gücü Potansi̇yeli̇ni̇n Tahmi̇ni̇

A Review Paper on Wind Climate Modeling

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